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From neuroscience to computer science: a topical approach on Twitter

Author

Listed:
  • C. A. Piña-García

    (Universidad Veracruzana
    Universidad Nacional Autónoma de México)

  • J. Mario Siqueiros-García

    (Universidad Nacional Autónoma de México)

  • E. Robles-Belmont

    (Universidad Nacional Autónoma de México)

  • Gustavo Carreón

    (Universidad Nacional Autónoma de México)

  • Carlos Gershenson

    (Universidad Nacional Autónoma de México
    Universidad Nacional Autónoma de México
    Massachusetts Institute of Technology
    ITMO University)

  • Julio Amador Díaz López

    (Imperial College London)

Abstract

Twitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.

Suggested Citation

  • C. A. Piña-García & J. Mario Siqueiros-García & E. Robles-Belmont & Gustavo Carreón & Carlos Gershenson & Julio Amador Díaz López, 2018. "From neuroscience to computer science: a topical approach on Twitter," Journal of Computational Social Science, Springer, vol. 1(1), pages 187-208, January.
    • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:1:d:10.1007_s42001-017-0002-9
    DOI: 10.1007/s42001-017-0002-9
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    References listed on IDEAS

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    1. David G Serfass & Ryne A Sherman, 2015. "Situations in 140 Characters: Assessing Real-World Situations on Twitter," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    2. Xin Shuai & Alberto Pepe & Johan Bollen, 2012. "How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
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    Cited by:

    1. J. Manuel Pérez-Verdejo & C. A. Piña-García & Mario Miguel Ojeda & A. Rivera-Lara & L. Méndez-Morales, 2021. "The rhythm of Mexico: an exploratory data analysis of Spotify’s top 50," Journal of Computational Social Science, Springer, vol. 4(1), pages 147-161, May.
    2. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.

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